ArticlePediatric cardiology2024
Machine Learning-Enabled Fully Automated Assessment of Left Ventricular Volume, Ejection Fraction and Strain: Experience in Pediatric and Young Adult Echocardiography.
Article in Pediatric cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Early Prediction of Cardiomyopathy in Pediatric Cancer Survivors Using Echocardiographic Video-Based Deep Learning.Echocardiography (Mount Kisco, N.Y.) · 2026Article
- The importance of the Autostrain RV technique in the treatment of right ventricular myocardial alterations in patients with breast cancer receiving chemotherapy.Scientific reports · 2025Article
- Artificial Intelligence in Fetal and Pediatric Echocardiography.Children (Basel, Switzerland) · 2024Review
- A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers.BMC medical imaging · 2024Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLeft ventricular (LV) volumes, ejection fraction (EF), and myocardial strain have been shown to be predictive of clinical and subclinical heart disease. Automation of LV functional assessment overcomes difficult technical challenges and complexities. We sought to assess whether a fully automated assessment of LV function could be reliably used in children and young adults.
methodsFifty normal volunteers (22/28, female/male) were prospectively recruited for research echocardiography. LV volumes, EF, and strain were measured both manually and automatically. An experienced sonographer performed all the manual analysis and recorded the analysis timing. The fully automated analyses were accomplished by 5 groups of observers with different knowledge and medical background. AutoLV and AutoSTRAIN (TomTec) were employed for the fully automated LV analysis. The LV volumes, EF, strain, and analysis time were compared between manual and automated methods, and among the 5 groups of observers.
resultsSoftware-determined endocardial border detection was achievable in all subjects. The analysis times of the experienced sonographer were significantly shorter for AutoLV and AutoSTRAIN than manual analyses (both p < 0.001). Strong correlations were seen between conventional EF and AutoLV (r = 0.8373), and between conventional three view global longitudinal strain (GLS) and AutoSTRAIN (r = 0.9766). The volumes from AutoLV and three view GLS from AutoSTRAIN had strong correlations among different observers regardless of level of expertise. EF from AutoLV analysis had moderately strong correlations among different observers.
conclusionAutomated pediatric LV analysis is feasible in normal hearts. Machine learning-enabled image analysis saves time and produces results that are comparable to traditional methods.
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